A new approach to predict the compression index using artificial intelligence methods

Benbouras, Mohammed Amin; Mitiche, R.; Zedira, Hamma; Petrişor, Alexandru-Ionuţ; Mezouar, N.; Debiche, Fatiha

doi:10.1080/1064119x.2018.1484533

Cited by 26 publications

(11 citation statements)

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“…Only the methods actually used are cited, followed by relevant references, which can be examined by the interested readers to better understand each one. The used methods are: Extreme Learning Machine (ELM) [40], Deep Neural Network (DNN) [6,41], Support Vector Regression (SVR) [42], Random Forest (RF) [43], LASSO regression (LASSO) [44], Partial Least Square Regression (PLS) [45], Ridge Regression (Ridge) [46], Kernel Ridge Regression (KRidge) [47],…”

Section: Machine Learning Methodsmentioning

confidence: 99%

“…Stepwise Regression (Stepwise) [48], and Genetic Programing (GP) [6]. Matlab software has been used for programming the algorithms corresponding to each method, except GP when the HeuristicLab Interface has been utilized [49].…”

Section: Machine Learning Methodsmentioning

confidence: 99%

“…On the other hand, the Oedometer test suffers from several disadvantages, such as the requirement of sophisticated and expensive equipment, being very time-consuming, and requiring highly qualified laboratory workers [4,5]. A well-known example is the consolidated clay, which could take up to 30 days when the Oedometer test is conducted [6]. However, the mechanical soil properties are sometimes inexistent or insufficient, in circumstances where the engineer must provide a quick and important decision to rapidly handle the critical situation.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of Swelling Index Using Advanced Machine Learning Techniques for Cohesive Soils

Benbouras

Petrişor

2021

Applied Sciences

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Several attempts have been made for estimating the vital swelling index parameter conducted by the expensive and time-consuming Oedometer test. However, they have only focused on the neuron network neglecting other advanced methods that could have increased the predictive capability of models. In order to overcome this limitation, the current study aims to elaborate an alternative model for estimating the swelling index from geotechnical physical parameters. The reliability of the approach is tested through several advanced machine learning methods like Extreme Learning Machine, Deep Neural Network, Support Vector Regression, Random Forest, LASSO regression, Partial Least Square Regression, Ridge Regression, Kernel Ridge, Stepwise Regression, Least Square Regression, and genetic Programing. These methods have been applied for modeling samples consisting of 875 Oedometer tests. Firstly, principal component analysis, Gamma test, and forward selection are utilized to reduce the input variable numbers. Afterward, the advanced techniques have been applied for modeling the proposed optimal inputs, and their accuracy models were evaluated through six statistical indicators and using K-fold cross validation approach. The comparative study shows the efficiency of FS-RF model. This elaborated model provided the most appropriate prediction, closest to the experimental values compared with other models and formulae proposed by the previous studies.

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Section: Machine Learning Methodsmentioning

confidence: 99%

Section: Machine Learning Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Prediction of Swelling Index Using Advanced Machine Learning Techniques for Cohesive Soils

Benbouras

Petrişor

2021

Applied Sciences

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“…Hence, only the utilized methods are mentioned below, followed by some relevant references, which could be observed by the concerned readers to perfectly understand each method. The methods used were Deep Neural Network (DNN) [43,44], Extreme Learning Machine (ELM) [45], Random Forest (RF) [46], Support Vector Regression (SVR) [47], Partial Least Square Regression (PLSR) [48], LASSO regression (LASSO) [49], Kernel Ridge Regression (KRidge) [50], Ridge Regression (Ridge) [51], Genetic Programming (GP) [43], and Stepwise Regression (Stepwise) [52]. Matlab has been applied for modeling the algorithms corresponding to each method, except for GP, where the HeuristicLab Interface has been utilized [53].…”

Section: Machine-learning Methodsmentioning

confidence: 99%

“…The findings prove that the significance is less than 0.05, except for X3, X4, and X5, showing that most correlations are statistically significant. Hence, according to Smith's classification (1986) [43], the pile-bearing capacity is significantly correlated with the input parameters, excluding X3, X4, and X5, which are poorly correlated. The results point out that these factors can have a complex nonlinear relationship with the pile-bearing capacity.…”

Section: Correlation Between Bearing Capacity and Input Parametersmentioning

confidence: 99%

Forecasting the Bearing Capacity of the Driven Piles Using Advanced Machine-Learning Techniques

et al. 2021

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Estimating the bearing capacity of piles is an essential point when seeking for safe and economic geotechnical structures. However, the traditional methods employed in this estimation are time-consuming and costly. The current study aims at elaborating a new alternative model for predicting the pile-bearing capacity based on eleven new advanced machine-learning methods in order to overcome these limitations. The modeling phase used a database of 100 samples collected from different countries. Additionally, eight relevant factors were selected in the input layer based on the literature recommendations. The optimal inputs were modeled using the machine-learning methods and their performance was assessed through six performance measures using a K-fold cross-validation approach. The comparative study proved the effectiveness of the DNN model, which displayed a higher performance in predicting the pile-bearing capacity. This elaborated model provided the optimal prediction, i.e., the closest to the experimental values, compared to the other models and formulae proposed by previous studies. Finally, a reliable and easy-to-use graphical interface was generated, namely “BeaCa2021”. This will be very helpful for researchers and civil engineers when estimating the pile-bearing capacity, with the advantage of saving time and money.

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Machine Learning Methods for Predicting Soil Compression Index

Akshaya,

Premalatha

2024

Lecture Notes in Civil Engineering

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A new approach to predict the compression index using artificial intelligence methods

Cited by 26 publications

References 50 publications

Prediction of Swelling Index Using Advanced Machine Learning Techniques for Cohesive Soils

Prediction of Swelling Index Using Advanced Machine Learning Techniques for Cohesive Soils

Forecasting the Bearing Capacity of the Driven Piles Using Advanced Machine-Learning Techniques

Machine Learning Methods for Predicting Soil Compression Index

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